The subjectively positive effects of drugs are thought to contribute to early stages of drug abuse. Both drug abuse and the initially positive response to drugs are variable in humans and are known to have a genetic component. Epidemiological studies have established that individuals who report having a positive experience with drugs are at increased risk to develop drug addiction. Accordingly, we and others have suggested that the subjectively positive response to drugs, or 'drug liking' represents an intermediate phenotype for drug abuse. Mice are powerful tools for studying the mammalian brain and the genetic basis of behavior. We will assess the motivational properties of methamphetamine (MA) in mice using the conditioned place preference (CPP) paradigm in which MA and saline are alternately paired with separate environments over a period of several days. On the last day the mouse is allowed to explore both environments, and preference for MA is measured as the amount of time spent in the drug-paired setting. CPP is widely used to study the rewarding effects of drugs in rodents and was recently demonstrated in a study of healthy human subjects. Importantly, self-reported preference for a drug-paired room is correlated with the self-reported pleasant effects of psychostimulants in humans. This suggests that in addition to other important aspects of reward such as incentive salience (drug wanting) and learning, CPP can be used to measure the hedonic properties of a rewarding drug. We propose a powerful systems genetics analysis of CPP in an advanced intercross line (AIL) of mice. AILs are generated by crossing two inbred strains for multiple generations and offer greater precision for mapping loci that influence quantitative traits (QTLs) than traditional genetic crosses. We will use a cutting- edge genotyping-by-sequencing (GBS) strategy to obtain genotypes for QTL mapping in 1,000 individuals. In a subset of these mice we will also measure gene expression in three brain regions critical to drug reward. Expression data will be generated using RNA-sequencing (RNAseq); those data will allow us to identify QTLs that regulate gene expression (eQTLs). Integrating genotype, phenotype and gene expression data is a powerful approach that will accelerate the process of identifying the genes that cause QTLs and provide insight into the biological mechanisms influencing the rewarding effects of drugs. Importantly, the proposed research provides opportunities for training in behavioral science, complex trait genetics, statistics and bioinformatics.